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Benchmarking and Enhancing Disentanglement in Concept-Residual Models
Concept bottleneck models (CBMs) are interpretable models that first predict a set of semantically meaningful features, i. e., concepts, from observations that are subsequently used to condition a downstream task.
Concept Learning for Interpretable Multi-Agent Reinforcement Learning
Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep neural network representations.
Normalizing Flows Across Dimensions
In this paper we introduce noisy injective flows (NIF), a generalization of normalizing flows that can go across dimensions.
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